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An Evidence Theory Based Embedding Model for the Management of Smart Water Environments.

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This study introduces a novel approach to manage water resources by integrating Internet of Things (IoT) data with advanced learning techniques. It addresses data uncertainties for better water management decisions.

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Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Water scarcity and quality degradation pose significant threats to human well-being and sustainable development.
  • Continuous Internet of Things (IoT)-based water measurements are vital for environmental monitoring but suffer from uncertainty issues.
  • Uncertainty in sensed water data can lead to biased analysis and flawed decision-making.

Purpose of the Study:

  • To develop a robust method for managing water resources by addressing uncertainty in IoT-based water measurements.
  • To enhance the accuracy and reliability of water information systems for effective decision-making.
  • To propose a framework combining network representation learning and uncertainty handling for water resource management.

Main Methods:

  • Utilizing network representation learning to model water information systems.
  • Incorporating probabilistic techniques to account for uncertainties in sensed water data.
  • Applying evidence theory for uncertainty-aware decision-making and strategy selection.

Main Results:

  • Development of a probabilistic embedding of the water network.
  • Classification of uncertain water information entities.
  • Enabling informed decision-making for water resource management strategies.

Conclusions:

  • Combining network representation learning and uncertainty handling provides a rigorous and efficient approach to water resource management.
  • The proposed method improves the reliability of water data analysis and decision-making processes.
  • This approach is crucial for sustainable water management in the face of increasing scarcity and data uncertainty.